对装甲车辆底盘发动机进行运行状态预测，提前了解发动机的健康状态，可以有效保障装甲车的作战能力，还能延长使用寿命。提出了一种轻量化的图卷积神经网络（LGCN），对装甲车底盘发动机的运行状态进行预测研究。首先，根据影响发动机运行状态的特征数据进行皮尔逊相关系数的量化分析。其次，基于特征相关性的量化结构构建图拉普拉斯矩阵。然后，引入了切比雪夫多项式减少谱域图卷积（GCN）计算过程的参数量和计算复杂度。最后，基于提出的轻量化图卷积神经网络对装甲车辆底盘发动机进行运行状态预测分析。结果表明，LGCN可以有效实现运行状态的预测分析，多模式识别算法的预测结果显示，LGCN获得了98.75%的分类准确率和98.31%F1分数，同时获得了最佳的预测稳定性。

Prediction of operating state for armored vehicle chassis engine and understanding the health state of engine in advance can effectively guarantee the combat capability of armored vehicle and extend its service life. A lightweight graph convolutional neural network (LGCN) was proposed to predict the operating state of an armored vehicle chassis engine. The quantitative analysis of Pearsons correlation coefficient was first carried out based on the characteristic data that affected the operating state of engine. The graph Laplacian matrix was constructed based on the quantitative structure of feature correlation. Then the Chebyshev polynomial was introduced to reduce the parameter amount and computation complexity of spectral domain graph convolution (GCN) calculation process. Finally, the operating state of armored vehicle chassis engine was predicted and analyzed based on the proposed lightweight graph convolutional neural network. The results show that LGCN can effectively realize the predictive analysis of operating state. The prediction results of multi-pattern recognition algorithm show that LGCN can obtain a classification accuracy of 98.75% and an F1-score of 98.31% with the best predictive stability.